Browsing: inference

Ask most executives to picture the cost of AI and they’ll likely see GPUs—racks of accelerators with gigawatts of power—and a waitlist for silicon. But that picture leaves something out: Data storage costs.

This post shows how to deploy one large language model (LLM) across two GPU nodes on Amazon Elastic Kubernetes Service (Amazon EKS) with the multi-node Ray LLM Deep Learning Containers. It uses separate prefill and decode nodes behind one OpenAI-compatible endpoint. This kind of multi-node setup is common for tensor, pipeline, and data parallelism, as…

AI inference infrastructure provider Modal Labs is nearing a $750 million funding round led by Accel at a $15.75 billion valuation that includes the investment, according to a been previously reported, though Axios and Bloomberg have reported other details of the deal

Eliminate GPU waste. Reduce first-token latency by up to 82%. Install one Kubernetes-native addon with zero application changes.

When you deploy a large language model (LLM) for inference on Amazon SageMaker HyperPod, there’s a gap between when you request a pod and when it’s ready to serve traffic. This gap is dominated by two sequential downloads: the inference server container image from Amazon Elastic Container Registry (Amazon ECR), and the model weights from…